Developing an LLM: Building, Training, Finetuning

2024-06-06 · Source: Sebastian Raschka · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Data Science & Analytics · Depth: Intermediate, extended

Summary

This content provides an overview of Large Language Model (LLM) development, detailing the three core stages: building, pre-training, and fine-tuning. It begins by outlining common LLM use cases, including public APIs like ChatGPT, running custom models locally (e.g., Llama 3 with `litgpt`), and deploying custom models on external servers for product development. The discussion then delves into the building stage, explaining LLMs as deep neural networks trained for next-word prediction, illustrating data preparation, batching for efficiency, and multi-word generation via iterative token prediction. It highlights the role of tokenization, data set sizes (e.g., GPT-3's 500 billion tokens, Llama 3's 15 trillion tokens), and architectural components like the Transformer block in GPT-2 and Llama 2 models. The pre-training stage focuses on creating foundation models, emphasizing the use of standard deep learning training loops and the concept of epochs. Finally, the fine-tuning stage covers adapting models for specific tasks like text classification (e.g., spam detection) and building personal assistants using instruction data sets, including an introduction to preference tuning for refining model responses and various evaluation metrics like MMLU, Alpaca Eval, and LLM-sis Chatbot Arena.

Key takeaway

For AI Scientists and Machine Learning Engineers considering LLM development, understanding the distinct stages of building, pre-training, and fine-tuning is crucial. You should prioritize leveraging pre-trained models for most applications, reserving full pre-training for novel architectures or foundational research. Focus on efficient fine-tuning techniques, such as partial fine-tuning or instruction-based methods, to adapt models for specific business cases like classification or chatbots, and utilize robust evaluation benchmarks to assess performance accurately.

Key insights

LLM development involves building architecture, pre-training on vast datasets, and fine-tuning for specific tasks and behaviors.

Principles

• LLMs predict the next token iteratively.
• Data quantity and quality drive LLM performance.
• Fine-tuning adapts foundation models efficiently.

Method

LLM development follows a three-stage process: building the architecture with attention mechanisms, pre-training on large datasets for next-token prediction, and fine-tuning with class labels or instruction datasets for specific applications.

In practice

• Use `litgpt` for local LLM interaction.
• Replace output layer for classification fine-tuning.
• Employ instruction datasets for chatbot development.

Topics

• LLM Development Lifecycle
• Transformer Architecture
• Pre-training Data
• Tokenization
• Instruction Fine-tuning

Best for: Machine Learning Engineer, AI Scientist, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Sebastian Raschka.